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NVIDIA NCA-GENL Exam - Topic 1 Question 1 Discussion

[Fundamentals of Machine Learning and Neural Networks]When comparing and contrasting the ReLU and sigmoid activation functions, which statement is true?
D) ReLU is more computationally efficient, but sigmoid is better for predicting probabilities.
A) ReLU is a linear function while sigmoid is non-linear.
B) ReLU is less computationally efficient than sigmoid, but it is more accurate than sigmoid.
C) ReLU and sigmoid both have a range of 0 to 1.

NVIDIA NCA-GENL Exam - Topic 1 Question 1 Discussion

Actual exam question for NVIDIA's NCA-GENL exam
Question #: 1
Topic #: 1
[All NCA-GENL Questions]

[Fundamentals of Machine Learning and Neural Networks]

When comparing and contrasting the ReLU and sigmoid activation functions, which statement is true?

Show Suggested Answer Hide Answer
Suggested Answer: D

ReLU (Rectified Linear Unit) and sigmoid are activation functions used in neural networks. According to NVIDIA's deep learning documentation (e.g., cuDNN and TensorRT), ReLU, defined as f(x) = max(0, x), is computationally efficient because it involves simple thresholding, avoiding expensive exponential calculations required by sigmoid, f(x) = 1/(1 + e^(-x)). Sigmoid outputs values in the range

[0, 1], making it suitable for predicting probabilities in binary classification tasks. ReLU, with an unbounded positive range, is less suited for direct probability prediction but accelerates training by mitigating vanishing gradient issues. Option A is incorrect, as ReLU is non-linear (piecewise linear). Option B is false, as ReLU is more efficient and not inherently more accurate. Option C is wrong, as ReLU's range is

[0, ), not

[0, 1].


NVIDIA cuDNN Documentation: https://docs.nvidia.com/deeplearning/cudnn/developer-guide/index.html

Goodfellow, I., et al. (2016). 'Deep Learning.' MIT Press.

by Sheridan at Jun 11, 2025, 11:00 AM

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Tequila
8 months ago
Sigmoid is non-linear, so that's a key difference!
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Salina
8 months ago
Wait, is ReLU really more accurate? That sounds off.
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Erin
9 months ago
I thought sigmoid was better for probabilities?
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Nelida
9 months ago
ReLU has a range from 0 to infinity, not 0 to 1.
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Cornell
9 months ago
ReLU is definitely more efficient than sigmoid!
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Flo
9 months ago
I’m confused about the ranges; I thought sigmoid was between 0 and 1, but ReLU can go higher than that. So maybe option C is wrong?
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Gregoria
9 months ago
I think I saw a practice question where it mentioned that sigmoid is good for probabilities, which makes me lean towards option D.
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Dylan
10 months ago
I’m not sure about the computational efficiency, but I feel like ReLU is generally faster than sigmoid.
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Thurman
10 months ago
I remember that ReLU is actually a non-linear function, so I think option A is incorrect.
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Clarinda
10 months ago
I think the range of the functions is an important distinction here. If I recall correctly, both ReLU and sigmoid have a range of 0 to 1, so option C seems like the safest bet.
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Gearldine
10 months ago
Okay, I remember learning that ReLU is more computationally efficient than sigmoid, so I'm leaning towards option D. The sigmoid function is better for predicting probabilities, right?
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Beckie
10 months ago
Hmm, I'm a bit confused on the computational efficiency part. I think option D might be correct, but I'll need to double-check the details on that.
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Corinne
11 months ago
I'm pretty sure the key difference is that ReLU is a linear function while sigmoid is non-linear, so I'll go with option A.
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Judy
1 year ago
Wait, are we sure this isn't a trick question? What if the answer is 'all of the above'? Just kidding, but it's always good to double-check!
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Mollie
11 months ago
Sigmoid is better for binary classification tasks because it squashes the output between 0 and 1.
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Caprice
1 year ago
ReLU is better for deep neural networks because it helps with the vanishing gradient problem.
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Jeff
1 year ago
I'd say C is the correct answer. Both functions have a range of 0 to 1, right? Seems pretty straightforward to me.
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Erick
1 year ago
D for sure! ReLU is faster, but sigmoid is the way to go if you need probabilities. Easy peasy!
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Dean
1 year ago
Hmm, I'm not sure. I guess I'll go with B since it sounds like the most comprehensive answer.
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Josue
11 months ago
User 2: I see your point, but I still think B is the best choice because it covers both the advantages and disadvantages of each activation function.
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Rachael
1 year ago
User 1: I think A is the correct answer because ReLU is better at addressing the vanishing gradient problem.
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Izetta
1 year ago
User 3: I'm going with B, it seems like the most comprehensive option.
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Peggie
1 year ago
User 2: I disagree, I believe D is the right choice.
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Silva
1 year ago
User 1: I think A is the correct answer.
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Andra
1 year ago
I'm not sure, but I think B) ReLU is less computationally efficient than sigmoid, but it is more accurate than sigmoid.
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Onita
1 year ago
I disagree, I believe the answer is D) ReLU is more computationally efficient, but sigmoid is better for predicting probabilities.
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Dana
1 year ago
I think the answer is A) ReLU is a linear function while sigmoid is non-linear.
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Ryan
1 year ago
I think the answer is D. ReLU is more efficient, but sigmoid is better for probabilities.
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Katheryn
1 year ago
Oh, I see. Thanks for clarifying that!
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William
1 year ago
Actually, the correct answer is A. ReLU is a linear function while sigmoid is non-linear.
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Magda
1 year ago
I think the answer is D. ReLU is more efficient, but sigmoid is better for probabilities.
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